METHODS: This study evaluated Malaysian Gelam honey as a nutraceutical alternative to estrogen HRT (ERT) in alleviating VVA. A total of 24 female 8-weekold Sprague Dawley rats underwent bilateral oophorectomy. A minimum of 14 days elapsed from the time of surgery and administration of the first dose of Gelam honey to allow the female hormones to subside to a stable baseline and complete recovery from surgery. Vaginal tissues were harvested following a 2-week administration of Gelam honey, the harvested vagina tissue underwent immunohistochemistry (IHC) analysis for protein localization and qPCR for mRNA expression analysis.
RESULTS: Results indicated that Gelam honey administration had increased the localization of Aqp1, Aqp5, CFTR and Muc1 proteins in vaginal tissue compared to the menopause group. The effect of Gelam honey on the protein expressions is summarized as Aqp1>CFTR>Aqp5>Muc1.
DISCUSSION: Gene expression analysis reveals Gelam honey had no effect on Aqp1 and CFTR genes. Gelam honey had up-regulated Aqp5 gene expression. However, its expression was lower than in the ERT+Ovx group. Additionally, Gelam honey up-regulated Muc1 in the vagina, with an expression level higher than those observed either in the ERT+Ovx or SC groups. Gelam honey exhibits a weak estrogenic effect on the genes and proteins responsible for regulating water in the vaginal tissue (Aqp1, Aqp5 and CFTR). In contrast, Gelam honey exhibits a strong estrogenic ability in influencing gene and protein expression for the sialic acid Muc1. Muc1 is associated with mucous production at the vaginal epithelial layer. In conclusion, the protein and gene expression changes in the vagina by Gelam honey had reduced the occurrence of vaginal atrophy in surgically-induced menopause models.
METHODS: This study proposed an end-to-end air quality predictive model for smart city applications, utilizing four machine learning techniques and two deep learning techniques. These include Ada Boost, SVR, RF, KNN, MLP regressor and LSTM. The study was conducted in four different urban cities in Selangor, Malaysia, including Petaling Jaya, Banting, Klang, and Shah Alam. The model considered the air quality data of various pollution markers such as PM2.5, PM10, O3, and CO. Additionally, meteorological data including wind speed and wind direction were also considered, and their interactions with the pollutant markers were quantified. The study aimed to determine the correlation variance of the dependent variable in predicting air pollution and proposed a feature optimization process to reduce dimensionality and remove irrelevant features to enhance the prediction of PM2.5, improving the existing LSTM model. The study estimates the concentration of pollutants in the air based on training and highlights the contribution of feature optimization in air quality predictions through feature dimension reductions.
RESULTS: In this section, the results of predicting the concentration of pollutants (PM2.5, PM10, O3, and CO) in the air are presented in R2 and RMSE. In predicting the PM10 and PM2.5concentration, LSTM performed the best overall high R2values in the four study areas with the R2 values of 0.998, 0.995, 0.918, and 0.993 in Banting, Petaling, Klang and Shah Alam stations, respectively. The study indicated that among the studied pollution markers, PM2.5,PM10, NO2, wind speed and humidity are the most important elements to monitor. By reducing the number of features used in the model the proposed feature optimization process can make the model more interpretable and provide insights into the most critical factor affecting air quality. Findings from this study can aid policymakers in understanding the underlying causes of air pollution and develop more effective smart strategies for reducing pollution levels.
METHODS: Thirty-six adult male rats were divided into six groups (n = 6): Control, Control EBN, Control E2, Wi-Fi, Wi-Fi+EBN, and Wi-Fi+E2. Control EBN and Wi-Fi+EBN groups received 250 mg/kg/day EBN, while Control E2 and Wi-Fi+E2 groups received 12 μg/kg/day E2 for 10 days. Wi-Fi exposure and EBN supplementation lasted eight weeks. Assessments included organ weight, hormone levels (FSH, LH, testosterone, and E2), ERα/ERβ mRNA and protein expression, spermatogenic markers (c-KIT and SCF), and sperm quality.
RESULTS: Wi-Fi exposure led to decreased FSH, testosterone, ERα mRNA, and sperm quality (concentration, motility, and viability). EBN supplementation restored serum FSH and testosterone levels, increased serum LH levels, and the testosterone/E2 ratio, and normalized mRNA ERα expression. Additionally, EBN increased sperm concentration in Wi-Fi-exposed rats without affecting motility or viability.
CONCLUSIONS: EBN plays a crucial role in regulating male reproductive hormones and spermatogenesis, leading to improved sperm concentration. This could notably benefit men experiencing oligospermia due to excessive Wi-Fi exposure.